Create app.py

app.py

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+# Use a pipeline as a high-level helper
+from transformers import pipeline
+import gradio as gr
+import pandas as pd
+import matplotlib.pyplot as plt
+
+analyser = pipeline("text-classification", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
+
+# model_path = ("./Models/models--distilbert--distilbert-base-uncased-finetuned-sst-2-english/snapshots/714eb0fa89d2f80546fda750413ed43d93601a13")
+
+# analyser = pipeline("text-classification", model=model_path)
+
+# print(analyser(["This product is good!", "This product is expensive!"]))
+
+
+def sentiment_analysis(text_to_review):
+    sentiment = analyser(text_to_review)
+    return sentiment[0]['label']
+
+# print(sentiment_analysis(["This product is good!", "This product is expensive!"]))
+
+def plot_sentiment_pie(df):
+    # Count the number of positive and negative reviews
+    sentiment_counts = df['Sentiment'].value_counts()
+    
+    # Create the pie chart
+    fig, ax = plt.subplots(figsize=(6, 6))
+    ax.pie(sentiment_counts.values, labels=sentiment_counts.index, autopct='%1.1f%%')
+    ax.set_title('Sentiment Distribution')
+    
+    # Convert the Matplotlib figure to a Gradio Plots component
+    return fig
+
+def read_excel_and_get_sentiment(file):
+    try:
+        df = pd.read_excel(file)
+        if 'Review' not in df.columns:
+            raise KeyError("'Review' column not found in the Excel file.")
+        df['Sentiment'] = df['Review'].apply(sentiment_analysis)
+
+        chart_object = plot_sentiment_pie(df)
+
+        return df, chart_object
+    except FileNotFoundError:
+        print(f"Error: {file} not found.")
+        raise
+    except Exception as e:
+        print(f"Error: {e}")
+        raise
+
+gr.close_all()
+
+demo = gr.Interface(fn=read_excel_and_get_sentiment,
+                    inputs=[gr.File(file_types= ['xlsx'],label="upload your review comment excel file.")],
+                    outputs=[gr.DataFrame(label="Reviewed text"), gr.Plot(label="Sentiment Analysis")],
+                    title="@IT AI Enthusiast (https://www.youtube.com/@itaienthusiast/) - Sentiment Analysis",
+                    description="THIS APPLICATION WILL BE USED TO ANALYZER THE SENTIMENT BASED ON THE COMMENT PROVIDER.",
+                    concurrency_limit=16)
+demo.launch()
+